Imaging Genetics of Brain Structure and Cognitive Aging in Murine Models of Alzheimer's Disease - Age-related cognitive decline (ARCD), Alzheimer disease (AD), and late-onset AD-related pathologies are linked to changes in brain structure, cell populations, synapse densities and connections, inflammation, protein aggregation and mitochondrial stress. However, we do not understand the complex causal networks and mechanisms of ARCD and AD. In this neurogenetics imaging program we quantify the impact of human familial AD (FAD) gene variants on brain structure and function using a highly diverse cohort of aging mouse hybrids that combine human genes variants with the BXD family. In Aims 1 and 2 we generate high resolution whole brain MRI DTI data and connectomes for each of 40 sex-matched sets of transgenic and aging control hybrids at ~6 and ~14 months using state-of-the art analysis workflows. We generate matched behavioral data, as well as light-sheet immunohistochemistry for entire brains taken from subsets of cases with the most outstanding phenotypes—lines that are highly susceptible to cognitive loss and those that are most resilient. Light-sheet, MRI-DTI and fMRI connectomes is merged with MI-DTI in Aim 3. All work exploits systems genetics and mapping methods we have developed and embedded in the GeneNetwork web service. A crucial facet of Aim 3 is integrating extensive behavioral data on age-related cognitive and other behavioral and CNS changes generated from AD-BXD and many other models. This allows us to define loci, candidate genes, and mechanisms modulating ARCD and AD, and to systematically test for associations with age, sex, and linked changes in structure, connectivity, and cell types. Finally, we integrate omics data we have for BXD and other genomes (e.g., hippocampal RNA-seq and proteomes) with comprehensive human AD GWAS, imaging, and omics data. All results are shared openly using robust internet services—GeneWeaver, CIVM server, NIF, Mouse Phenome Database, and the AMP-AD Knowledge Portal. Data and workflows will be FAIR-compliant. Key deliverables are (1) far more quantitative, unbiased, global, and replicable data on genetic, molecular, cellular, and system-wide processes linked to cognitive loss and AD. We also deliver causal molecular and mechanistic models of that incorporate realistically high levels of genetic diversity—6 million DNA variants. This work empowers in-depth unbiased analyses of age-related functional decline in ARCD and AD that translate to human populations. Success will enable faster and more robust preclinical testing of interventions and drug treatments for ARCD and AD.
